The main challenge/objective is to detect similar labels across different users in FL heterogeneous settings without the knowledge of local user data. This necessitates us to construct anonymized data without transferring raw sensitive data, and identify new class similarities on the anonymized data. We motivate our framework from the creation of Data Impressions (DI) using zero-shot learning as proposed in [1]} to compute Anonymized Data Impressions. Let us assume a model \(\mathcal {M}\)  with input \(\mathbf {X}\)  and output \(\mathbf {y}\) , where \(\mathbf {X} \in \mathcal {R}^{M\times N}\)  is the set of features and \(\mathbf {y}\in \mathcal {R}^M\) . Now, the anonymized feature set \(\bar{\mathbf {X}}\) , which has same properties of \(\mathbf {X}\) , can be synthesized in two steps:
<TABLE>